论文标题
一种用于标记不完整投标卡特尔的机器学习方法
A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels
论文作者
论文摘要
我们提出了一种标记出价索具的新方法，这对于检测不完整的投标卡特尔特别有用。我们的方法结合了屏幕，即从招标中出价的分布得出的统计数据，以及机器学习以预测勾结的可能性。作为一种方法上的创新，我们计算了招标中三到四个出价的所有可能子组的此类屏幕，并使用摘要统计数据，例如均值，中位数，最大和最小值的每个屏幕作为机器学习算法中的预测指标。这种方法解决了在不完整的卡特尔中竞标竞标的问题，扭曲了出价索具产生的统计信号。我们证明，根据瑞士的经验数据，我们的算法以前提出了用于不完整卡特尔的应用中建议的方法。
We propose a new method for flagging bid rigging, which is particularly useful for detecting incomplete bid-rigging cartels. Our approach combines screens, i.e. statistics derived from the distribution of bids in a tender, with machine learning to predict the probability of collusion. As a methodological innovation, we calculate such screens for all possible subgroups of three or four bids within a tender and use summary statistics like the mean, median, maximum, and minimum of each screen as predictors in the machine learning algorithm. This approach tackles the issue that competitive bids in incomplete cartels distort the statistical signals produced by bid rigging. We demonstrate that our algorithm outperforms previously suggested methods in applications to incomplete cartels based on empirical data from Switzerland.
